Introduction

A hospitality-focused intelligence platform that enables hotels and OTAs to turn multilingual guest reviews into measurable service improvements. Using advanced NLP and ML, the solution extracts high-precision sentiment insights and feeds them into recommendation systems to improve personalization and drive higher guest satisfaction. 

Problem Statement

A platform that enables hotels and OTAs to improve guest satisfaction by converting multilingual, unstructured reviews into actionable insights making it possible to interpret sentiment and context at scale with far greater accuracy than traditional tools.

Challenges:

• NLP complexity across multiple languages and dialects.
  (Different platforms generate varied writing styles and emotions, making sentiment detection difficult without domain-trained models.)

  
  

• Required hospitality-specific taxonomy with high precision.
  (Generic NLP models do not understand nuances like “late check-in,” “housekeeping turnaround,” or “amenity satisfaction.”)

  
  

• Difficulty in continuous training with new review data.
  (Guest sentiment evolves seasonally; models need frequent updates to stay accurate.) 

  
  

• Weighted ranking of reviews from different platforms.
  (Reviews from OTAs, Google, TripAdvisor, and brand sites each carry different credibility and influence levels.)